2 December 2016

Airbnb in Berlin

  • 20,000+ Berliners hosted more than half a million guests in 2015
  • Renting out flats as a source of income has become more and more popular.
  • "Zweckentfremdungsverbot" law was passed in May 2014 but only came into force in April 2016.

Airbnb in Berlin


Source: www.insideairbnb.com

Evidence from Literature

  • Airbnb drives up rent: Schäfer et al. (2016) found that rent growth is higher in neighbourhoods that have a significant number of misused flats.
  • Effect on the hotel industry: Guttentag (2015) estimates that Airbnb sold about 15 million room nights in 2012.
  • Evidence from Texas: Zervas, Proserpio, and Byers (2016) found that a 10 percent size increase of the Airbnb market in Texas resulted in a .39 percent decrease in hotel revenue.

Research Question & Hypothesis




What is the effect of Airbnb on Hotels in Berlin?

The higher the Airbnb supply in a given district in Berlin, the greater the negative effect on the hotel industry in that same district.

Data & Variables

Data from the Statistical Information System Berlin/Brandenburg (SBB), the Federal Statistical Office and the statistical offices of the Länder (FSO), InsideAirbnb.com, & Eurostat.

Dependent variable:

\[{Occupancy Rate}_{it} = \frac {{Overnight Stays}_{it}}{{Hotel Beds}_{it}*{days}_{t}}\]

Main independent variables:

Airbnb Supply: Cumulative sum of new listings

Dynamic Airbnb Supply: Active listings based on reviews

Airbnb listings per Neighbourhood (static model) (2010 - 2014)

Airbnb listings per Neighbourhood (dynamic model) (2010 - 2014)

Effect of Increase in Airbnb Listings on Berlin Hotel Occupancy Rates (2010 - 2014)

Regression Model

Fixed Effects (for time and district) to account for unobserved heterogeneity:

\({\log Occupancy Rate}_{it} = \beta _i * \log AbbSupply_{it} + \beta _j * X' + \tau _i + \varepsilon _{it}\)

\({X'}\): Control variables (UE rate, average HH income, passengers arriving in Berlin)

\({\tau _i}\): Time and district dummies

Estimation Results

Dependent variable:
Occupancy Rate
(1) (2) (3)
Log Airbnb Listings 0.010*** -0.010*** -0.008***
(0.004) (0.002) (0.003)
Average HH Income (Log) -0.154* -0.280*** -0.291***
(0.088) (0.096) (0.097)
Unemployment Rate 0.143 0.351* 0.310
(0.152) (0.194) (0.197)
Incoming Passengers -0.002
(0.006)
Market Entry 0.00000*** 0.00000*** 0.00000***
(0.00000) (0.000) (0.000)
marketentry -0.008
(0.007)
Neighbourhood-specific trend Yes No No
Time trend Yes No No
District Time FE? No No
Observations 720 720 720
R2 0.996 0.790 0.791
Adjusted R2 0.995 0.773 0.772
Note: p<0.1; p<0.05; p<0.01

Prelimenary Conclusion

  • Test

Thank you!

Questions?

Further Readings

Guttentag, Daniel. 2015. “Airbnb: Disruptive Innovation and the Rise of an Informal Tourism Accommodation Sector.” Current Issues in Tourism 18 (12). Taylor & Francis: 1192–1217.

Schäfer, Philipp, Nicole Braun, Richard Reed, and Nicole Johnston. 2016. “Misuse Through Short-Term Rentals on the Berlin Housing Market.” International Journal of Housing Markets and Analysis 9 (2). Emerald Group Publishing Limited.

Zervas, Georgios, Davide Proserpio, and John Byers. 2016. “The Rise of the Sharing Economy: Estimating the Impact of Airbnb on the Hotel Industry.” Boston U. School of Management Research Paper, no. 2013-16.